





'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class  lowerCAmelCase_		(		unittest.TestCase		):




 def 				_snake_case (						self		)     ->				List[Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




 @property
 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   1
     _lowerCAmelCase						      =   3
     _lowerCAmelCase						      =   (32, 32)

     _lowerCAmelCase						      =   floats_tensor((batch_size, num_channels) + sizes      ,  rng=random.Random(0		)		).to(_lowerCAmelCase		)
     return image




 @property
 def 				_snake_case (						self		)     ->				Optional[int]:
     torch.manual_seed(0		)
     _lowerCAmelCase						      =   UNetaDConditionModel(
         block_out_channels=(32, 32, 64)      ,  layers_per_block=2      ,  sample_size=32      ,  in_channels=7      ,  out_channels=4      ,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D")      ,  up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D")      ,  cross_attention_dim=32      ,  attention_head_dim=8      ,  use_linear_projection=_lowerCAmelCase      ,  only_cross_attention=(True, True, False)      ,  num_class_embeds=100      ,  )
     return model




 @property
 def 				_snake_case (						self		)     ->				Optional[Any]:
     torch.manual_seed(0		)
     _lowerCAmelCase						      =   AutoencoderKL(
         block_out_channels=[32, 32, 64]      ,  in_channels=3      ,  out_channels=3      ,  down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"]      ,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]      ,  latent_channels=4      ,  )
     return model




 @property
 def 				_snake_case (						self		)     ->				str:
     torch.manual_seed(0		)
     _lowerCAmelCase						      =   CLIPTextConfig(
         bos_token_id=0      ,  eos_token_id=2      ,  hidden_size=32      ,  intermediate_size=37      ,  layer_norm_eps=1E-05      ,  num_attention_heads=4      ,  num_hidden_layers=5      ,  pad_token_id=1      ,  vocab_size=1000      ,  hidden_act="gelu"      ,  projection_dim=512      ,  )
     return CLIPTextModel(_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   "cpu"  # ensure determinism for the device-dependent torch.Generator
     _lowerCAmelCase						      =   self.dummy_cond_unet_upscale
     _lowerCAmelCase						      =   DDPMScheduler()
     _lowerCAmelCase						      =   DDIMScheduler(prediction_type="v_prediction"		)
     _lowerCAmelCase						      =   self.dummy_vae
     _lowerCAmelCase						      =   self.dummy_text_encoder
     _lowerCAmelCase						      =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

     _lowerCAmelCase						      =   self.dummy_image.cpu().permute(0      ,  2      ,  3      ,  1		)[0]
     _lowerCAmelCase						      =   Image.fromarray(np.uinta(_lowerCAmelCase		)		).convert("RGB"		).resize((64, 64)		)

     # make sure here that pndm scheduler skips prk
     _lowerCAmelCase						      =   StableDiffusionUpscalePipeline(
         unet=_lowerCAmelCase      ,  low_res_scheduler=_lowerCAmelCase      ,  scheduler=_lowerCAmelCase      ,  vae=_lowerCAmelCase      ,  text_encoder=_lowerCAmelCase      ,  tokenizer=_lowerCAmelCase      ,  max_noise_level=350      ,  )
     _lowerCAmelCase						      =   sd_pipe.to(_lowerCAmelCase		)
     sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

     _lowerCAmelCase						      =   "A painting of a squirrel eating a burger"
     _lowerCAmelCase						      =   torch.Generator(device=_lowerCAmelCase		).manual_seed(0		)
     _lowerCAmelCase						      =   sd_pipe(
         [prompt]      ,  image=_lowerCAmelCase      ,  generator=_lowerCAmelCase      ,  guidance_scale=6.0      ,  noise_level=20      ,  num_inference_steps=2      ,  output_type="np"      ,  )

     _lowerCAmelCase						      =   output.images

     _lowerCAmelCase						      =   torch.Generator(device=_lowerCAmelCase		).manual_seed(0		)
     _lowerCAmelCase						      =   sd_pipe(
         [prompt]      ,  image=_lowerCAmelCase      ,  generator=_lowerCAmelCase      ,  guidance_scale=6.0      ,  noise_level=20      ,  num_inference_steps=2      ,  output_type="np"      ,  return_dict=_lowerCAmelCase      ,  )[0]

     _lowerCAmelCase						      =   image[0, -3:, -3:, -1]
     _lowerCAmelCase						      =   image_from_tuple[0, -3:, -3:, -1]

     _lowerCAmelCase						      =   low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)
     _lowerCAmelCase						      =   np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]		)

     assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2




 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   "cpu"  # ensure determinism for the device-dependent torch.Generator
     _lowerCAmelCase						      =   self.dummy_cond_unet_upscale
     _lowerCAmelCase						      =   DDPMScheduler()
     _lowerCAmelCase						      =   DDIMScheduler(prediction_type="v_prediction"		)
     _lowerCAmelCase						      =   self.dummy_vae
     _lowerCAmelCase						      =   self.dummy_text_encoder
     _lowerCAmelCase						      =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

     _lowerCAmelCase						      =   self.dummy_image.cpu().permute(0      ,  2      ,  3      ,  1		)[0]
     _lowerCAmelCase						      =   Image.fromarray(np.uinta(_lowerCAmelCase		)		).convert("RGB"		).resize((64, 64)		)

     # make sure here that pndm scheduler skips prk
     _lowerCAmelCase						      =   StableDiffusionUpscalePipeline(
         unet=_lowerCAmelCase      ,  low_res_scheduler=_lowerCAmelCase      ,  scheduler=_lowerCAmelCase      ,  vae=_lowerCAmelCase      ,  text_encoder=_lowerCAmelCase      ,  tokenizer=_lowerCAmelCase      ,  max_noise_level=350      ,  )
     _lowerCAmelCase						      =   sd_pipe.to(_lowerCAmelCase		)
     sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

     _lowerCAmelCase						      =   "A painting of a squirrel eating a burger"
     _lowerCAmelCase						      =   sd_pipe(
         2 * [prompt]      ,  image=2 * [low_res_image]      ,  guidance_scale=6.0      ,  noise_level=20      ,  num_inference_steps=2      ,  output_type="np"      ,  )
     _lowerCAmelCase						      =   output.images
     assert image.shape[0] == 2

     _lowerCAmelCase						      =   torch.Generator(device=_lowerCAmelCase		).manual_seed(0		)
     _lowerCAmelCase						      =   sd_pipe(
         [prompt]      ,  image=_lowerCAmelCase      ,  generator=_lowerCAmelCase      ,  num_images_per_prompt=2      ,  guidance_scale=6.0      ,  noise_level=20      ,  num_inference_steps=2      ,  output_type="np"      ,  )
     _lowerCAmelCase						      =   output.images
     assert image.shape[0] == 2





 @unittest.skipIf(torch_device != "cuda"      ,  "This test requires a GPU"		)
 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   self.dummy_cond_unet_upscale
     _lowerCAmelCase						      =   DDPMScheduler()
     _lowerCAmelCase						      =   DDIMScheduler(prediction_type="v_prediction"		)
     _lowerCAmelCase						      =   self.dummy_vae
     _lowerCAmelCase						      =   self.dummy_text_encoder
     _lowerCAmelCase						      =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

     _lowerCAmelCase						      =   self.dummy_image.cpu().permute(0      ,  2      ,  3      ,  1		)[0]
     _lowerCAmelCase						      =   Image.fromarray(np.uinta(_lowerCAmelCase		)		).convert("RGB"		).resize((64, 64)		)

     # put models in fp16, except vae as it overflows in fp16
     _lowerCAmelCase						      =   unet.half()
     _lowerCAmelCase						      =   text_encoder.half()

     # make sure here that pndm scheduler skips prk
     _lowerCAmelCase						      =   StableDiffusionUpscalePipeline(
         unet=_lowerCAmelCase      ,  low_res_scheduler=_lowerCAmelCase      ,  scheduler=_lowerCAmelCase      ,  vae=_lowerCAmelCase      ,  text_encoder=_lowerCAmelCase      ,  tokenizer=_lowerCAmelCase      ,  max_noise_level=350      ,  )
     _lowerCAmelCase						      =   sd_pipe.to(_lowerCAmelCase		)
     sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

     _lowerCAmelCase						      =   "A painting of a squirrel eating a burger"
     _lowerCAmelCase						      =   torch.manual_seed(0		)
     _lowerCAmelCase						      =   sd_pipe(
         [prompt]      ,  image=_lowerCAmelCase      ,  generator=_lowerCAmelCase      ,  num_inference_steps=2      ,  output_type="np"      ,  ).images

     _lowerCAmelCase						      =   low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)







@slow
@require_torch_gpu
class  lowerCAmelCase_		(		unittest.TestCase		):




 def 				_snake_case (						self		)     ->				int:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/sd2-upscale/low_res_cat.png"		)
     _lowerCAmelCase						      =   load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
         "/upsampled_cat.npy"		)

     _lowerCAmelCase						      =   "stabilityai/stable-diffusion-x4-upscaler"
     _lowerCAmelCase						      =   StableDiffusionUpscalePipeline.from_pretrained(_lowerCAmelCase		)
     pipe.to(_lowerCAmelCase		)
     pipe.set_progress_bar_config(disable=_lowerCAmelCase		)
     pipe.enable_attention_slicing()

     _lowerCAmelCase						      =   "a cat sitting on a park bench"

     _lowerCAmelCase						      =   torch.manual_seed(0		)
     _lowerCAmelCase						      =   pipe(
         prompt=_lowerCAmelCase      ,  image=_lowerCAmelCase      ,  generator=_lowerCAmelCase      ,  output_type="np"      ,  )
     _lowerCAmelCase						      =   output.images[0]

     assert image.shape == (512, 512, 3)
     assert np.abs(expected_image - image		).max() < 1E-3




 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/sd2-upscale/low_res_cat.png"		)
     _lowerCAmelCase						      =   load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
         "/upsampled_cat_fp16.npy"		)

     _lowerCAmelCase						      =   "stabilityai/stable-diffusion-x4-upscaler"
     _lowerCAmelCase						      =   StableDiffusionUpscalePipeline.from_pretrained(
         _lowerCAmelCase      ,  torch_dtype=torch.floataa      ,  )
     pipe.to(_lowerCAmelCase		)
     pipe.set_progress_bar_config(disable=_lowerCAmelCase		)
     pipe.enable_attention_slicing()

     _lowerCAmelCase						      =   "a cat sitting on a park bench"

     _lowerCAmelCase						      =   torch.manual_seed(0		)
     _lowerCAmelCase						      =   pipe(
         prompt=_lowerCAmelCase      ,  image=_lowerCAmelCase      ,  generator=_lowerCAmelCase      ,  output_type="np"      ,  )
     _lowerCAmelCase						      =   output.images[0]

     assert image.shape == (512, 512, 3)
     assert np.abs(expected_image - image		).max() < 5E-1





 def 				_snake_case (						self		)     ->				int:
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     _lowerCAmelCase						      =   load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/sd2-upscale/low_res_cat.png"		)

     _lowerCAmelCase						      =   "stabilityai/stable-diffusion-x4-upscaler"
     _lowerCAmelCase						      =   StableDiffusionUpscalePipeline.from_pretrained(
         _lowerCAmelCase      ,  torch_dtype=torch.floataa      ,  )
     pipe.to(_lowerCAmelCase		)
     pipe.set_progress_bar_config(disable=_lowerCAmelCase		)
     pipe.enable_attention_slicing(1		)
     pipe.enable_sequential_cpu_offload()

     _lowerCAmelCase						      =   "a cat sitting on a park bench"

     _lowerCAmelCase						      =   torch.manual_seed(0		)
     _lowerCAmelCase						      =   pipe(
         prompt=_lowerCAmelCase      ,  image=_lowerCAmelCase      ,  generator=_lowerCAmelCase      ,  num_inference_steps=5      ,  output_type="np"      ,  )

     _lowerCAmelCase						      =   torch.cuda.max_memory_allocated()
     # make sure that less than 2.9 GB is allocated
     assert mem_bytes < 2.9 * 10**9







'''simple docstring'''

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
			if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
			from .pipeline_unclip import UnCLIPPipeline
			from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
			from .text_proj import UnCLIPTextProjModel




